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Data-driven algorithms play a large role in decision making across a varietyof industries. Increasingly, these algorithms are being used to make decisionsthat have significant ramifications for people's social and economicwell-being, e.g. in sentencing, loan approval, and policing. Amid theproliferation of such systems there is a growing concern about their potentialdiscriminatory impact. In particular, machine learning systems which aretrained on biased data have the potential to learn and perpetuate those biases.A central challenge for practitioners is thus to determine whether their modelsdisplay discriminatory bias. Here we present a case study in which we frame theissue of bias detection as a causal inference problem with observational data.We enumerate two main causes of bias, sampling bias and label bias, and weinvestigate the abilities of six different fairness metrics to detect each biastype. Based on these investigations, we propose a set of best practiceguidelines to select the fairness metric that is most likely to detect bias ifit is present. Additionally, we aim to identify the conditions in which certainfairness metrics may fail to detect bias and instead give practitioners a falsebelief that their biased model is making fair decisions.

Evaluating Fairness Metrics in the Presence of Dataset Bias